diff --git a/NEWS.md b/NEWS.md
index d8edd70b6..594403165 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -29,6 +29,7 @@
 
 - Brought the docs on `alpha_sd` up to date with the code change from prior PR #853. By @zsusswein in #862 and reviewed by @jamesmbaazam.
 - The `...` argument in `estimate_secondary()` has been removed because it was not used. By @jamesmbaazam in #894 and reviewed by @.
+- All examples now use the natural parameters of distributions rather than the mean and standard deviation when specifying uncertain distributions. This is to eliminate warnings and encourage best practice. By @jamesmbaazam in #893 and reviewed by @sbfnk.
 
 # EpiNow2 1.6.1
 
diff --git a/R/dist_spec.R b/R/dist_spec.R
index 5b9a155ba..cce218198 100644
--- a/R/dist_spec.R
+++ b/R/dist_spec.R
@@ -116,9 +116,12 @@ discrete_pmf <- function(distribution =
 #' )
 #' dist1 + dist1
 #'
-#' # An uncertain gamma distribution with mean 3 and sd 2
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
 #' dist2 <- Gamma(
-#'   mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+#'   shape = Normal(3, 0.5),
+#'   rate = Normal(2, 0.5),
+#'   max = 20
 #' )
 #' dist1 + dist2
 `+.dist_spec` <- function(e1, e2) {
@@ -197,9 +200,12 @@ discrete_pmf <- function(distribution =
 #' )
 #' dist1 + dist1
 #'
-#' # An uncertain gamma distribution with mean 3 and sd 2
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
 #' dist2 <- Gamma(
-#'   mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+#'   shape = Normal(3, 0.5),
+#'   rate = Normal(2, 0.5),
+#'   max = 20
 #' )
 #' c(dist1, dist2)
 c.dist_spec <- function(...) {
@@ -257,9 +263,12 @@ c.dist_spec <- function(...) {
 #' dist1 <- LogNormal(mean = 5, sd = 1, max = 20)
 #' mean(dist1)
 #'
-#' # An uncertain gamma distribution with mean 3 and sd 2
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
 #' dist2 <- Gamma(
-#'  mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+#'   shape = Normal(3, 0.5),
+#'   rate = Normal(2, 0.5),
+#'   max = 20
 #' )
 #' mean(dist2)
 #'
@@ -393,8 +402,13 @@ sd.default <- function(x, ...) {
 #' dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 #' max(dist1)
 #'
-#' # An uncertain lognormal distribution with mean 3 and sd 2
-#' dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
+#' # An uncertain lognormal distribution with meanlog and sdlog normally
+#' # distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+#' dist2 <- LogNormal(
+#'   meanlog = Normal(3, 0.5),
+#'   sdlog = Normal(2, 0.5),
+#'   max = 20
+#' )
 #' max(dist2)
 #'
 #' # The max the sum of two distributions
@@ -447,8 +461,13 @@ discretise <- function(x, ...) {
 #' # A fixed gamma distribution with mean 5 and sd 1.
 #' dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 #'
-#' # An uncertain lognormal distribution with mean 3 and sd 2
-#' dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
+#' # An uncertain lognormal distribution with meanlog and sdlog normally
+#' # distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+#' dist2 <- LogNormal(
+#'   meanlog = Normal(3, 0.5),
+#'   sdlog = Normal(2, 0.5),
+#'   max = 20
+#' )
 #'
 #' # The maxf the sum of two distributions
 #' discretise(dist1 + dist2, strict = FALSE)
@@ -531,11 +550,16 @@ collapse <- function(x, ...) {
 #' # A fixed gamma distribution with mean 5 and sd 1.
 #' dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 #'
-#' # An uncertain lognormal distribution with mean 3 and sd 2
-#' dist2 <- LogNormal(mean = 3, sd = 2, max = 20)
+#' # An uncertain lognormal distribution with meanlog and sdlog normally
+#' # distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+#' dist2 <- LogNormal(
+#'   meanlog = Normal(3, 0.5),
+#'   sdlog = Normal(2, 0.5),
+#'   max = 20
+#' )
 #'
 #' # The maxf the sum of two distributions
-#' collapse(discretise(dist1 + dist2))
+#' collapse(discretise(dist1 + dist2, strict = FALSE))
 collapse.dist_spec <- function(x, ...) {
   return(x)
 }
@@ -588,9 +612,10 @@ collapse.multi_dist_spec <- function(x, ...) {
 #' dist1 <- LogNormal(mean = 1.5, sd = 0.5, max = 20)
 #' print(dist1)
 #'
-#' # An uncertain gamma distribution with mean 3 and sd 2
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
 #' dist2 <- Gamma(
-#'   mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+#'   shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 20
 #' )
 #' print(dist2)
 print.dist_spec <- function(x, ...) {
@@ -680,9 +705,12 @@ print.dist_spec <- function(x, ...) {
 #' # Plot discretised distribution with 0.01 day discretisation window
 #' plot(dist1, res = 0.01, cumulative = FALSE)
 #'
-#' # An uncertain gamma distribution with mean 3 and sd 2
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
 #' dist2 <- Gamma(
-#'   mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+#'   shape = Normal(3, 0.5),
+#'   rate = Normal(2, 0.5),
+#'   max = 20
 #' )
 #' plot(dist2)
 #'
@@ -781,9 +809,12 @@ plot.dist_spec <- function(x, samples = 50L, res = 1, cumulative = TRUE, ...) {
 #' @examples
 #' dist1 <- LogNormal(mean = 1.6, sd = 0.5, max = 20)
 #'
-#' # An uncertain gamma distribution with mean 3 and sd 2
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
 #' dist2 <- Gamma(
-#'   mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+#'   shape = Normal(3, 0.5),
+#'   rate = Normal(2, 0.5),
+#'   max = 20
 #' )
 #'
 #' # Multiple distributions
@@ -828,9 +859,12 @@ fix_parameters <- function(x, ...) {
 #' @importFrom rlang arg_match
 #' @method fix_parameters dist_spec
 #' @examples
-#' # An uncertain gamma distribution with mean 3 and sd 2
-#' dist <- LogNormal(
-#'   meanlog = Normal(3, 0.5), sdlog = Normal(2, 0.5), max = 20
+#' # An uncertain gamma distribution with shape and rate normally distributed
+#' # as Normal(3, 0.5) and Normal(2, 0.5) respectively
+#' dist <- Gamma(
+#'   shape = Normal(3, 0.5),
+#'   rate = Normal(2, 0.5),
+#'   max = 20
 #' )
 #'
 #' fix_parameters(dist)
@@ -891,8 +925,13 @@ is_constrained <- function(x, ...) {
 #' # A fixed gamma distribution with mean 5 and sd 1.
 #' dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 #'
-#' # An uncertain lognormal distribution with mean 3 and sd 2
-#' dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
+#' # An uncertain lognormal distribution with meanlog and sdlog normally
+#' # distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+#' dist2 <- LogNormal(
+#'   meanlog = Normal(3, 0.5),
+#'   sdlog = Normal(2, 0.5),
+#'   max = 20
+#' )
 #'
 #' # both distributions are constrained and therefore so is the sum
 #' is_constrained(dist1 + dist2)
@@ -952,7 +991,8 @@ is_constrained.multi_dist_spec <- function(x, ...) {
 #' @examples
 #' LogNormal(mean = 4, sd = 1)
 #' LogNormal(mean = 4, sd = 1, max = 10)
-#' LogNormal(mean = Normal(4, 1), sd = 1, max = 10)
+#' # If specifying uncertain parameters, use the natural parameters
+#' LogNormal(meanlog = Normal(1.5, 0.5), sdlog = 0.25, max = 10)
 LogNormal <- function(meanlog, sdlog, mean, sd, ...) {
   params <- as.list(environment())
   return(new_dist_spec(params, "lognormal", ...))
diff --git a/R/opts.R b/R/opts.R
index 04d9e5577..613a6452f 100644
--- a/R/opts.R
+++ b/R/opts.R
@@ -42,8 +42,8 @@
 #' # An uncertain gamma distributed generation time
 #' generation_time_opts(
 #'   Gamma(
-#'     mean = Normal(mean = 3, sd = 1),
-#'     sd = Normal(mean = 2, sd = 0.5),
+#'     shape = Normal(mean = 3, sd = 1),
+#'     rate = Normal(mean = 2, sd = 0.5),
 #'     max = 14
 #'   )
 #' )
@@ -186,7 +186,11 @@ secondary_opts <- function(type = c("incidence", "prevalence"), ...) {
 #' delay_opts()
 #'
 #' # A single delay that has uncertainty
-#' delay <- LogNormal(mean = Normal(1, 0.2), sd = Normal(0.5, 0.1), max = 14)
+#' delay <- LogNormal(
+#'   meanlog = Normal(1, 0.2),
+#'   sdlog = Normal(0.5, 0.1),
+#'   max = 14
+#' )
 #' delay_opts(delay)
 #'
 #' # A single delay without uncertainty
diff --git a/man/Distributions.Rd b/man/Distributions.Rd
index 583b0193d..add503e54 100644
--- a/man/Distributions.Rd
+++ b/man/Distributions.Rd
@@ -77,7 +77,8 @@ probability mass function is given directly as a numeric vector.
 \examples{
 LogNormal(mean = 4, sd = 1)
 LogNormal(mean = 4, sd = 1, max = 10)
-LogNormal(mean = Normal(4, 1), sd = 1, max = 10)
+# If specifying uncertain parameters, use the natural parameters
+LogNormal(meanlog = Normal(1.5, 0.5), sdlog = 0.25, max = 10)
 Gamma(mean = 4, sd = 1)
 Gamma(shape = 16, rate = 4)
 Gamma(shape = Normal(16, 2), rate = Normal(4, 1))
diff --git a/man/c.dist_spec.Rd b/man/c.dist_spec.Rd
index 45cffbf55..ae603e9e2 100644
--- a/man/c.dist_spec.Rd
+++ b/man/c.dist_spec.Rd
@@ -27,9 +27,12 @@ dist1 <- LogNormal(
 )
 dist1 + dist1
 
-# An uncertain gamma distribution with mean 3 and sd 2
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
 dist2 <- Gamma(
-  mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+  shape = Normal(3, 0.5),
+  rate = Normal(2, 0.5),
+  max = 20
 )
 c(dist1, dist2)
 }
diff --git a/man/collapse.Rd b/man/collapse.Rd
index 97ed0d1c2..9715d9c33 100644
--- a/man/collapse.Rd
+++ b/man/collapse.Rd
@@ -25,9 +25,14 @@ in the <dist_spec>.
 # A fixed gamma distribution with mean 5 and sd 1.
 dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 
-# An uncertain lognormal distribution with mean 3 and sd 2
-dist2 <- LogNormal(mean = 3, sd = 2, max = 20)
+# An uncertain lognormal distribution with meanlog and sdlog normally
+# distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+dist2 <- LogNormal(
+  meanlog = Normal(3, 0.5),
+  sdlog = Normal(2, 0.5),
+  max = 20
+)
 
 # The maxf the sum of two distributions
-collapse(discretise(dist1 + dist2))
+collapse(discretise(dist1 + dist2, strict = FALSE))
 }
diff --git a/man/delay_opts.Rd b/man/delay_opts.Rd
index b28a47b72..dc383a541 100644
--- a/man/delay_opts.Rd
+++ b/man/delay_opts.Rd
@@ -44,7 +44,11 @@ functions.
 delay_opts()
 
 # A single delay that has uncertainty
-delay <- LogNormal(mean = Normal(1, 0.2), sd = Normal(0.5, 0.1), max = 14)
+delay <- LogNormal(
+  meanlog = Normal(1, 0.2),
+  sdlog = Normal(0.5, 0.1),
+  max = 14
+)
 delay_opts(delay)
 
 # A single delay without uncertainty
diff --git a/man/discretise.Rd b/man/discretise.Rd
index e6c2cda1a..a76646d4a 100644
--- a/man/discretise.Rd
+++ b/man/discretise.Rd
@@ -42,8 +42,13 @@ from the difference of two censored events.
 # A fixed gamma distribution with mean 5 and sd 1.
 dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 
-# An uncertain lognormal distribution with mean 3 and sd 2
-dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
+# An uncertain lognormal distribution with meanlog and sdlog normally
+# distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+dist2 <- LogNormal(
+  meanlog = Normal(3, 0.5),
+  sdlog = Normal(2, 0.5),
+  max = 20
+)
 
 # The maxf the sum of two distributions
 discretise(dist1 + dist2, strict = FALSE)
diff --git a/man/extract_single_dist.Rd b/man/extract_single_dist.Rd
index e3407cf6b..e6761e96c 100644
--- a/man/extract_single_dist.Rd
+++ b/man/extract_single_dist.Rd
@@ -20,9 +20,12 @@ A single \code{dist_spec} object
 \examples{
 dist1 <- LogNormal(mean = 1.6, sd = 0.5, max = 20)
 
-# An uncertain gamma distribution with mean 3 and sd 2
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
 dist2 <- Gamma(
-  mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+  shape = Normal(3, 0.5),
+  rate = Normal(2, 0.5),
+  max = 20
 )
 
 # Multiple distributions
diff --git a/man/fix_parameters.Rd b/man/fix_parameters.Rd
index 04a8a4136..a43df5bbd 100644
--- a/man/fix_parameters.Rd
+++ b/man/fix_parameters.Rd
@@ -25,9 +25,12 @@ If the given \verb{<dist_spec>} has any uncertainty, it is removed and the
 corresponding distribution converted into a fixed one.
 }
 \examples{
-# An uncertain gamma distribution with mean 3 and sd 2
-dist <- LogNormal(
-  meanlog = Normal(3, 0.5), sdlog = Normal(2, 0.5), max = 20
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
+dist <- Gamma(
+  shape = Normal(3, 0.5),
+  rate = Normal(2, 0.5),
+  max = 20
 )
 
 fix_parameters(dist)
diff --git a/man/generation_time_opts.Rd b/man/generation_time_opts.Rd
index 1200101c7..8306d7a61 100644
--- a/man/generation_time_opts.Rd
+++ b/man/generation_time_opts.Rd
@@ -79,8 +79,8 @@ generation_time_opts(Gamma(mean = 3, sd = 2, max = 14))
 # An uncertain gamma distributed generation time
 generation_time_opts(
   Gamma(
-    mean = Normal(mean = 3, sd = 1),
-    sd = Normal(mean = 2, sd = 0.5),
+    shape = Normal(mean = 3, sd = 1),
+    rate = Normal(mean = 2, sd = 0.5),
     max = 14
   )
 )
diff --git a/man/is_constrained.Rd b/man/is_constrained.Rd
index 7a097ab6d..39803ee62 100644
--- a/man/is_constrained.Rd
+++ b/man/is_constrained.Rd
@@ -23,8 +23,13 @@ Logical; TRUE if \code{x} is constrained
 # A fixed gamma distribution with mean 5 and sd 1.
 dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 
-# An uncertain lognormal distribution with mean 3 and sd 2
-dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
+# An uncertain lognormal distribution with meanlog and sdlog normally
+# distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+dist2 <- LogNormal(
+  meanlog = Normal(3, 0.5),
+  sdlog = Normal(2, 0.5),
+  max = 20
+)
 
 # both distributions are constrained and therefore so is the sum
 is_constrained(dist1 + dist2)
diff --git a/man/max.dist_spec.Rd b/man/max.dist_spec.Rd
index c81f811ef..516a0cc3a 100644
--- a/man/max.dist_spec.Rd
+++ b/man/max.dist_spec.Rd
@@ -25,8 +25,13 @@ in parameters)
 dist1 <- Gamma(mean = 5, sd = 1, max = 20)
 max(dist1)
 
-# An uncertain lognormal distribution with mean 3 and sd 2
-dist2 <- LogNormal(mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20)
+# An uncertain lognormal distribution with meanlog and sdlog normally
+# distributed as Normal(3, 0.5) and Normal(2, 0.5) respectively
+dist2 <- LogNormal(
+  meanlog = Normal(3, 0.5),
+  sdlog = Normal(2, 0.5),
+  max = 20
+)
 max(dist2)
 
 # The max the sum of two distributions
diff --git a/man/mean.dist_spec.Rd b/man/mean.dist_spec.Rd
index 5448ca1b8..8be98f2d0 100644
--- a/man/mean.dist_spec.Rd
+++ b/man/mean.dist_spec.Rd
@@ -25,9 +25,12 @@ distributions combined in the passed <dist_spec>.
 dist1 <- LogNormal(mean = 5, sd = 1, max = 20)
 mean(dist1)
 
-# An uncertain gamma distribution with mean 3 and sd 2
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
 dist2 <- Gamma(
- mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+  shape = Normal(3, 0.5),
+  rate = Normal(2, 0.5),
+  max = 20
 )
 mean(dist2)
 
diff --git a/man/plot.dist_spec.Rd b/man/plot.dist_spec.Rd
index 27698649a..7e1a8d19e 100644
--- a/man/plot.dist_spec.Rd
+++ b/man/plot.dist_spec.Rd
@@ -33,9 +33,12 @@ plot(dist1)
 # Plot discretised distribution with 0.01 day discretisation window
 plot(dist1, res = 0.01, cumulative = FALSE)
 
-# An uncertain gamma distribution with mean 3 and sd 2
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
 dist2 <- Gamma(
-  mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+  shape = Normal(3, 0.5),
+  rate = Normal(2, 0.5),
+  max = 20
 )
 plot(dist2)
 
diff --git a/man/plus-.dist_spec.Rd b/man/plus-.dist_spec.Rd
index 16e967faa..091b7e536 100644
--- a/man/plus-.dist_spec.Rd
+++ b/man/plus-.dist_spec.Rd
@@ -26,9 +26,12 @@ dist1 <- LogNormal(
 )
 dist1 + dist1
 
-# An uncertain gamma distribution with mean 3 and sd 2
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
 dist2 <- Gamma(
-  mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+  shape = Normal(3, 0.5),
+  rate = Normal(2, 0.5),
+  max = 20
 )
 dist1 + dist2
 }
diff --git a/man/print.dist_spec.Rd b/man/print.dist_spec.Rd
index c3ea43cfb..b6925af9f 100644
--- a/man/print.dist_spec.Rd
+++ b/man/print.dist_spec.Rd
@@ -24,9 +24,10 @@ functions of fixed delay distributions combined in the passed <dist_spec>.
 dist1 <- LogNormal(mean = 1.5, sd = 0.5, max = 20)
 print(dist1)
 
-# An uncertain gamma distribution with mean 3 and sd 2
+# An uncertain gamma distribution with shape and rate normally distributed
+# as Normal(3, 0.5) and Normal(2, 0.5) respectively
 dist2 <- Gamma(
-  mean = Normal(3, 0.5), sd = Normal(2, 0.5), max = 20
+  shape = Normal(3, 0.5), rate = Normal(2, 0.5), max = 20
 )
 print(dist2)
 }
diff --git a/vignettes/estimate_infections_options.Rmd.orig b/vignettes/estimate_infections_options.Rmd.orig
index 25706703e..0cd532013 100644
--- a/vignettes/estimate_infections_options.Rmd.orig
+++ b/vignettes/estimate_infections_options.Rmd.orig
@@ -172,8 +172,8 @@ Here, instead of doing so we assume that we know about truncation with mean of 1
 
 ```{r define_truncation}
 trunc_dist <- LogNormal(
-  mean = Normal(0.5, 0.1),
-  sd = Normal(0.5, 0.1),
+  mean = 0.5,
+  sd = 0.5,
   max = 3
 )
 trunc_dist
diff --git a/vignettes/estimate_infections_workflow.Rmd.orig b/vignettes/estimate_infections_workflow.Rmd.orig
index 2c8f48a19..6f28d6cec 100644
--- a/vignettes/estimate_infections_workflow.Rmd.orig
+++ b/vignettes/estimate_infections_workflow.Rmd.orig
@@ -90,10 +90,10 @@ plot(fixed_gamma)
 ```
 
 If distributions are variable, the values with uncertainty are treated as [prior probability densities](https://en.wikipedia.org/wiki/Prior_probability) in the Bayesian inference framework used by _EpiNow2_, i.e. they are estimated as part of the inference.
-For example, to define a variable gamma distribution where uncertainty in the mean is given by a normal distribution with mean 3 and sd 2, and uncertainty in the standard deviation is given by a normal distribution with mean 1 and sd 0.1, with a maximum value 10, you would write
+For example, to define a variable gamma distribution where uncertainty in the shape is given by a normal distribution with mean 3 and sd 2, and uncertainty in the rate is given by a normal distribution with mean 1 and sd 0.1, with a maximum value 10, you would write
 
 ```{r}
-uncertain_gamma <- Gamma(mean = Normal(3, 2), sd = Normal(1, 0.1), max = 10)
+uncertain_gamma <- Gamma(shape = Normal(3, 2), rate = Normal(1, 0.1), max = 10)
 uncertain_gamma
 ```
 
@@ -102,10 +102,6 @@ which looks like this when plotted
 ```{r plot_uncertain_gamma}
 plot(uncertain_gamma)
 ```
-
-Note the warning about parameters.
-We used the mean and standard deviation to define this distribution with uncertain parameters, but it would be better to use the "natural" parameters of the gamma distribution, shape and rate, for example using the values estimate and reported back after calling the previous command.
-
 There are various ways the specific delay distributions mentioned below might be obtained.
 Often, they will come directly from the existing literature reviewed by the user and studies conducted elsewhere.
 Sometimes it might be possible to obtain them from existing databases, e.g. using the [epiparameter](https://github.com/epiverse-trace/epiparameter) R package.
@@ -209,7 +205,7 @@ It can be changed using the `rt_opts()` function.
 For example, if the user believes that at the very start of the data the reproduction number was 2, with uncertainty in this belief represented by a standard deviation of 1, they would use
 
 ```{r results = 'hide'}
-rt_prior <- LogNormal(mean =  2, sd = 1)
+rt_prior <- LogNormal(mean = 2, sd = 1)
 rt_opts(prior = rt_prior)
 ```